Enterprise Data & AI Integration Architect

World Vision International (New); Dubai; On-site; AED 220,000 - 360,000; Full time

Job summary

World Vision International seeks an Enterprise Data & AI Integration Architect to shape and execute NorthStar data and AI architecture across BDAT domains. You will define target-state architectures, drive modernization, enforce governance, security and responsible AI, and mentor delivery teams working on diverse, global platforms.

You will translate business strategy into concrete architectural guidance, establish reusable patterns, and ensure scalable, cost-effective, and interoperable

Qualifications

• Typically 10+ years of progressive experience across data architecture, data engineering, analytics platforms, enterprise architecture, or AI/ML architecture.
• At least 5 years leading enterprise-scale architecture decisions with current/target state planning.
• Strong practical knowledge of cloud data platforms, lakehouse patterns, data integration, APIs, streaming, and governance.
• Experience designing AI/ML or generative AI foundations with production data pipelines and observability.
• Excellent command of English and ability to influence senior stakeholders.

Responsibilities

• Define and maintain the enterprise Data and AI architecture strategy, models, standards, and roadmaps.
• Identify dependencies across architecture domains and guide modernization investments.
• Advise senior leadership on platform strategy, governance, and risk implications.
• Ensure security, privacy, resilience, and regulatory compliance are baked in by design.
• Mentor architects and delivery teams across global programs.

Skills

Data architecture

Data engineering

Analytics platforms

Enterprise architecture

AI/ML architecture

Cloud data platforms

Lakehouse/Warehouse patterns

Data integration

Event-driven architectures

APIs

Streaming

Security & privacy

Governance

Responsible AI

Knowledge graphs

Cloud platforms: AWS

Cloud platforms: Azure

Tools

AWS

Azure

Job description

JOB PURPOSE

The Enterprise Data amp AI Integration Architect defines and evolves the organization s target-state data integration analytics and AI architecture The role connects business strategy to executable technology choices across data platforms integration information architecture governance analytics machine learning generative AI and knowledge systems The role operates across all architecture domains Business Data Application and Technology BDAT with particular depth in Data Domain Strategic enough to shape investment and governance and hands-on enough to validate designs prototype critical patterns lead complex global solution design and guide delivery teams

Acting as a trusted advisor and design authority the architect establishes reusable standards and guardrails leads current-state target-state and transition planning assures major solution designs and enables teams to deliver secure interoperable cost-effective and AI-ready capabilities

The architect provides technical leadership and coordination across a defined portfolio programme or architectural domain segment or global platforms ensuring consistency quality and alignment of solution architectures within their scope translating enterprise direction into practical guidance for delivery teams This role bridges enterprise architecture intent and solution execution

Within the data specialization the role provides architectural leadership for enterprise data design data management and data governance ensuring solutions align with data standards regulatory requirements and approved enterprise architecture direction This specialization focuses on embedding sound data architecture practices across initiatives while supporting consistent scalable and sustainable technology outcomes

Role mandate

Create a coherent enterprise data and AI architecture blueprint with a pragmatic multi-year roadmap from current state to target state to achieve and document NorthStar Architecture vision

Modernize the data landscape so governed high-quality data can support operational use cases analytics machine learning generative AI and agentic workflows

Reduce fragmentation duplicated platforms tech debt inconsistent definitions uncontrolled data movement and architecture debt in partnership with data governance

Embed security privacy resilience regulatory compliance responsible AI observability and cost management into architecture by design

Increase delivery speed through reference architectures approved patterns data products reusable components and clear engineering standards

Translate complex architectural trade-offs into decisions that executives business leaders risk teams product owners and engineers can act on

Ensure decision support for data governance the glossary and data strategy

Support the development of EA principles and human-AI interaction principles and guidelines

Master and support the development of the WVI EA Method

Mentor or guide aspiring architects or other professionals

KEY RESPONSIBILITIES

Enterprise strategy and roadmaps

Own the enterprise Data and AI architecture strategy principles reference architecture and models standards and roadmap aligned with business priorities transformation outcomes risk and investment constraints

Identify impacts dependencies and constraints across all other architecture domains BDAT and security

Assess the current data estate identify capability gaps and technical debt define target-state and transition architectures and sequence modernization into achievable investment increments

Advise senior leadership on platform strategy operating model sourcing build-versus-buy choices vendor concentration and the business implications of fragmented or under-governed data

Maintain decision records capability maps architecture debt registers and measurable adoption plans so strategy remains connected to delivery

Required Professional Experience

Typically 10+ years of progressive experience across data architecture, data engineering, analytics platforms, enterprise architecture, or AI/ML architecture, including at least 5 years leading enterprise-scale architecture decisions. Demonstrated ownership of current-state, target-state, and transition architectures for complex, multi-system data environments. Strong practical knowledge of modern cloud data platforms, lakehouse/warehouse patterns, data integration, serverless, MSA integration , event driven architectures, integration modernization, APIs, streaming, orchestration, metadata, lineage, data quality, and master/reference data. Experience designing AI/ML or generative AI foundations, including production data pipelines, retrieval patterns, evaluation, deployment, observability, and governance controls using cloud-native and cloud-agnostic technologies. Cloud experience in AWS/ Azure .Deep understanding of security, privacy, resilience, regulatory compliance, and responsible AI requirements in enterprise environments. Ability to elicit and translate business capabilities and non-functional requirements into architecture decisions, standards, roadmaps, and executable delivery guidance. Evidence of influencing senior stakeholders, facilitating cross-functional decisions, mentoring technical teams, and resolving ambiguity without relying on formal authority. Required Language(s) Excellent command of spoken and written English, with the ability to communicate clearly and effectively with diverse stakeholders.

Preferred Experience, Knowledge and/or other Qualifications

Architecture experience in a regulated or data-sensitive industry such as non-profit, financial services, insurance, healthcare, pharmaceuticals, legal/professional services, public sector, or telecommunications. Experience building or materially modernizing an enterprise data and AI capability from the ground up, including operating model and governance adoption not only technical platform delivery. Hands‑on experience validating architecture through prototypes, reference implementations, design spikes, or production delivery leadership. Experience with knowledge graphs, semantic technologies, agentic AI, data mesh/product operating models, FinOps, or AI observability. Consulting or executive‑advisory experience, including presentation of investment options, risk, and value to C-level audiences.